Pandas Profiling Report

Dataset statistics

Number of variables	12
Number of observations	56
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	5.4 KiB
Average record size in memory	98.3 B

Variable types

Numeric	12

Warnings

`Train Number` is highly correlated with `Average passengers per day non peak season` and 6 other fields	High correlation
`Average passengers per day non peak season` is highly correlated with `Train Number` and 10 other fields	High correlation
`Average Kms Per Day` is highly correlated with `Average passengers per day non peak season` and 6 other fields	High correlation
`Yearly Passenger In Million` is highly correlated with `Train Number` and 10 other fields	High correlation
`Passenger Kilometers` is highly correlated with `Train Number` and 10 other fields	High correlation
`Fuel Consumption in Litres` is highly correlated with `Average passengers per day non peak season` and 7 other fields	High correlation
`Electricity Consumption in Units` is highly correlated with `Average passengers per day non peak season` and 8 other fields	High correlation
`Average Lead Distance` is highly correlated with `Train Number` and 6 other fields	High correlation
`Average Time Delay in Minutes Yearly` is highly correlated with `Train Number` and 8 other fields	High correlation
`Average Lead Time in Mins Yearly` is highly correlated with `Train Number` and 10 other fields	High correlation
`Earnings in Crs` is highly correlated with `Average passengers per day non peak season` and 6 other fields	High correlation
`Average rate per passenger km in paise` is highly correlated with `Train Number` and 7 other fields	High correlation
`Train Number` is uniformly distributed	Uniform
`Train Number` has unique values	Unique
`Yearly Passenger In Million` has unique values	Unique
`Passenger Kilometers` has unique values	Unique
`Fuel Consumption in Litres` has unique values	Unique
`Electricity Consumption in Units` has unique values	Unique
`Average Lead Time in Mins Yearly` has unique values	Unique
`Earnings in Crs` has unique values	Unique

Reproduction

Analysis started	2021-03-11 12:02:21.585117
Analysis finished	2021-03-11 12:02:44.144584
Duration	22.56 seconds
Software version	pandas-profiling v2.11.0
Download configuration	config.yaml

Train Number
Real number (ℝ_≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct	56
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	1258.5
Minimum	1231
Maximum	1286
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	1231
5-th percentile	1233.75
Q1	1244.75
median	1258.5
Q3	1272.25
95-th percentile	1283.25
Maximum	1286
Range	55
Interquartile range (IQR)	27.5

Descriptive statistics

Standard deviation	16.30950643
Coefficient of variation (CV)	0.01295948068
Kurtosis	-1.2
Mean	1258.5
Median Absolute Deviation (MAD)	14
Skewness	0
Sum	70476
Variance	266
Monotocity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1280	1	1.8%
1281	1	1.8%
1254	1	1.8%
1255	1	1.8%
1256	1	1.8%
1257	1	1.8%
1258	1	1.8%
1259	1	1.8%
1260	1	1.8%
1261	1	1.8%
Other values (46)	46	82.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1231	1	1.8%
1232	1	1.8%
1233	1	1.8%
1234	1	1.8%
1235	1	1.8%

Value	Count	Frequency (%)
1286	1	1.8%
1285	1	1.8%
1284	1	1.8%
1283	1	1.8%
1282	1	1.8%

Average passengers per day non peak season
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	55
Distinct (%)	98.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	2178.339286
Minimum	412
Maximum	4552
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	412
5-th percentile	743
Q1	1348.5
median	2046.5
Q3	2793.5
95-th percentile	4140
Maximum	4552
Range	4140
Interquartile range (IQR)	1445

Descriptive statistics

Standard deviation	1048.958675
Coefficient of variation (CV)	0.4815405394
Kurtosis	-0.3423830251
Mean	2178.339286
Median Absolute Deviation (MAD)	748
Skewness	0.4873672491
Sum	121987
Variance	1100314.301
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1884	2	3.6%
3329	1	1.8%
3178	1	1.8%
1106	1	1.8%
2259	1	1.8%
2005	1	1.8%
3802	1	1.8%
1373	1	1.8%
680	1	1.8%
2017	1	1.8%
Other values (45)	45	80.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
412	1	1.8%
499	1	1.8%
680	1	1.8%
764	1	1.8%
808	1	1.8%

Value	Count	Frequency (%)
4552	1	1.8%
4477	1	1.8%
4377	1	1.8%
4061	1	1.8%
3876	1	1.8%

Average Kms Per Day
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	55
Distinct (%)	98.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	1738.214286
Minimum	776
Maximum	3944
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	776
5-th percentile	924.5
Q1	1213.75
median	1562
Q3	1853.5
95-th percentile	3653.75
Maximum	3944
Range	3168
Interquartile range (IQR)	639.75

Descriptive statistics

Standard deviation	786.3194982
Coefficient of variation (CV)	0.4523720146
Kurtosis	1.74970066
Mean	1738.214286
Median Absolute Deviation (MAD)	351.5
Skewness	1.527329883
Sum	97340
Variance	618298.3532
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1637	2	3.6%
3118	1	1.8%
2396	1	1.8%
1602	1	1.8%
3944	1	1.8%
1485	1	1.8%
1606	1	1.8%
1613	1	1.8%
2126	1	1.8%
1743	1	1.8%
Other values (45)	45	80.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
776	1	1.8%
872	1	1.8%
914	1	1.8%
928	1	1.8%
942	1	1.8%

Value	Count	Frequency (%)
3944	1	1.8%
3847	1	1.8%
3845	1	1.8%
3590	1	1.8%
3370	1	1.8%

Yearly Passenger In Million
Real number (ℝ_≥0)

HIGH CORRELATION
UNIQUE

Distinct	56
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	3916.553571
Minimum	1275
Maximum	8421
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	1275
5-th percentile	1667.5
Q1	2509.75
median	3654
Q3	4647
95-th percentile	7794.25
Maximum	8421
Range	7146
Interquartile range (IQR)	2137.25

Descriptive statistics

Standard deviation	1810.605905
Coefficient of variation (CV)	0.4622957076
Kurtosis	0.450576713
Mean	3916.553571
Median Absolute Deviation (MAD)	1148.5
Skewness	0.9404270775
Sum	219327
Variance	3278293.743
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2945	1	1.8%
5378	1	1.8%
1992	1	1.8%
6219	1	1.8%
8397	1	1.8%
7246	1	1.8%
1872	1	1.8%
3792	1	1.8%
2257	1	1.8%
4049	1	1.8%
Other values (46)	46	82.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1275	1	1.8%
1284	1	1.8%
1594	1	1.8%
1692	1	1.8%
1750	1	1.8%

Value	Count	Frequency (%)
8421	1	1.8%
8397	1	1.8%
8224	1	1.8%
7651	1	1.8%
7246	1	1.8%

Passenger Kilometers
Real number (ℝ_≥0)

HIGH CORRELATION
UNIQUE

Distinct	56
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	59683.39286
Minimum	6551
Maximum	168589
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	6551
5-th percentile	12893.5
Q1	26009.5
median	47134
Q3	86017.5
95-th percentile	138859.5
Maximum	168589
Range	162038
Interquartile range (IQR)	60008

Descriptive statistics

Standard deviation	41027.75023
Coefficient of variation (CV)	0.6874232222
Kurtosis	-0.1690254382
Mean	59683.39286
Median Absolute Deviation (MAD)	27842.5
Skewness	0.8136218146
Sum	3342270
Variance	1683276289
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
28037	1	1.8%
39433	1	1.8%
63045	1	1.8%
51912	1	1.8%
38730	1	1.8%
73292	1	1.8%
103759	1	1.8%
22163	1	1.8%
13268	1	1.8%
85066	1	1.8%
Other values (46)	46	82.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6551	1	1.8%
8165	1	1.8%
11770	1	1.8%
13268	1	1.8%
13561	1	1.8%

Value	Count	Frequency (%)
168589	1	1.8%
145654	1	1.8%
144057	1	1.8%
137127	1	1.8%
130917	1	1.8%

Fuel Consumption in Litres
Real number (ℝ_≥0)

HIGH CORRELATION
UNIQUE

Distinct	56
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	282103.5893
Minimum	54235
Maximum	990153
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	54235
5-th percentile	67936.5
Q1	100583.5
median	201615.5
Q3	351237.5
95-th percentile	856652
Maximum	990153
Range	935918
Interquartile range (IQR)	250654

Descriptive statistics

Standard deviation	246202.4754
Coefficient of variation (CV)	0.8727378337
Kurtosis	1.543877476
Mean	282103.5893
Median Absolute Deviation (MAD)	108472
Skewness	1.532670644
Sum	15797801
Variance	6.061565889 × 10¹⁰
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
345600	1	1.8%
87425	1	1.8%
115899	1	1.8%
59966	1	1.8%
236066	1	1.8%
902465	1	1.8%
772548	1	1.8%
180808	1	1.8%
990153	1	1.8%
424778	1	1.8%
Other values (46)	46	82.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
54235	1	1.8%
59966	1	1.8%
65895	1	1.8%
68617	1	1.8%
70430	1	1.8%

Value	Count	Frequency (%)
990153	1	1.8%
952449	1	1.8%
902465	1	1.8%
841381	1	1.8%
772548	1	1.8%

Electricity Consumption in Units
Real number (ℝ_≥0)

HIGH CORRELATION
UNIQUE

Distinct	56
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	341786.9821
Minimum	62400
Maximum	1158742
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	62400
5-th percentile	80830
Q1	126022.75
median	248574.5
Q3	437255
95-th percentile	995511.5
Maximum	1158742
Range	1096342
Interquartile range (IQR)	311232.25

Descriptive statistics

Standard deviation	286158.7887
Coefficient of variation (CV)	0.8372430889
Kurtosis	1.274785716
Mean	341786.9821
Median Absolute Deviation (MAD)	133257.5
Skewness	1.43385896
Sum	19140071
Variance	8.188685234 × 10¹⁰
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
102145	1	1.8%
490912	1	1.8%
1158742	1	1.8%
300103	1	1.8%
430666	1	1.8%
978508	1	1.8%
269389	1	1.8%
66517	1	1.8%
575702	1	1.8%
222935	1	1.8%
Other values (46)	46	82.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
62400	1	1.8%
66517	1	1.8%
77665	1	1.8%
81885	1	1.8%
83991	1	1.8%

Value	Count	Frequency (%)
1158742	1	1.8%
1098103	1	1.8%
1046522	1	1.8%
978508	1	1.8%
903465	1	1.8%

Average Lead Distance
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	48
Distinct (%)	85.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	24.65714286
Minimum	15.9
Maximum	37
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	15.9
5-th percentile	16.55
Q1	19.175
median	24
Q3	30.85
95-th percentile	33.125
Maximum	37
Range	21.1
Interquartile range (IQR)	11.675

Descriptive statistics

Standard deviation	6.136651653
Coefficient of variation (CV)	0.2488792675
Kurtosis	-1.390149015
Mean	24.65714286
Median Absolute Deviation (MAD)	5.7
Skewness	0.177715333
Sum	1380.8
Variance	37.65849351
Monotocity	Not monotonic

Histogram with fixed size bins (bins=48)

Value	Count	Frequency (%)
29.7	2	3.6%
24	2	3.6%
33.8	2	3.6%
32.5	2	3.6%
20.4	2	3.6%
31	2	3.6%
16.4	2	3.6%
20.6	2	3.6%
19.1	1	1.8%
29.5	1	1.8%
Other values (38)	38	67.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
15.9	1	1.8%
16.4	2	3.6%
16.6	1	1.8%
16.8	1	1.8%
16.9	1	1.8%

Value	Count	Frequency (%)
37	1	1.8%
33.8	2	3.6%
32.9	1	1.8%
32.8	1	1.8%
32.7	1	1.8%

Average Time Delay in Minutes Yearly
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	55
Distinct (%)	98.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	139.5785714
Minimum	68.8
Maximum	257.5
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	68.8
5-th percentile	73.45
Q1	83.275
median	128.95
Q3	187.55
95-th percentile	234.45
Maximum	257.5
Range	188.7
Interquartile range (IQR)	104.275

Descriptive statistics

Standard deviation	58.94213407
Coefficient of variation (CV)	0.4222864116
Kurtosis	-1.276494855
Mean	139.5785714
Median Absolute Deviation (MAD)	50.75
Skewness	0.3895009584
Sum	7816.4
Variance	3474.175169
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
74.8	2	3.6%
72.1	1	1.8%
169.3	1	1.8%
74.4	1	1.8%
141.6	1	1.8%
99.8	1	1.8%
178	1	1.8%
208.6	1	1.8%
147.6	1	1.8%
69.9	1	1.8%
Other values (45)	45	80.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
68.8	1	1.8%
69.9	1	1.8%
72.1	1	1.8%
73.9	1	1.8%
74.4	1	1.8%

Value	Count	Frequency (%)
257.5	1	1.8%
241.5	1	1.8%
234.6	1	1.8%
234.4	1	1.8%
229.3	1	1.8%

Average Lead Time in Mins Yearly
Real number (ℝ_≥0)

HIGH CORRELATION
UNIQUE

Distinct	56
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	75.36428571
Minimum	46.2
Maximum	138
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	46.2
5-th percentile	47.2
Q1	50.125
median	70.55
Q3	94.075
95-th percentile	127.375
Maximum	138
Range	91.8
Interquartile range (IQR)	43.95

Descriptive statistics

Standard deviation	27.47692869
Coefficient of variation (CV)	0.3645881923
Kurtosis	-0.6990821697
Mean	75.36428571
Median Absolute Deviation (MAD)	21.1
Skewness	0.7086805295
Sum	4220.4
Variance	754.9816104
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
46.6	1	1.8%
67	1	1.8%
71.4	1	1.8%
86	1	1.8%
47.3	1	1.8%
62.1	1	1.8%
70.1	1	1.8%
68	1	1.8%
94.6	1	1.8%
118	1	1.8%
Other values (46)	46	82.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
46.2	1	1.8%
46.6	1	1.8%
46.9	1	1.8%
47.3	1	1.8%
47.4	1	1.8%

Value	Count	Frequency (%)
138	1	1.8%
130.4	1	1.8%
127.9	1	1.8%
127.2	1	1.8%
124.7	1	1.8%

Earnings in Crs
Real number (ℝ_≥0)

HIGH CORRELATION
UNIQUE

Distinct	56
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	6504.748214
Minimum	98.2
Maximum	36532.3
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	98.2
5-th percentile	146.075
Q1	337.875
median	1829.55
Q3	9787.5
95-th percentile	26340.8
Maximum	36532.3
Range	36434.1
Interquartile range (IQR)	9449.625

Descriptive statistics

Standard deviation	9088.261979
Coefficient of variation (CV)	1.397173523
Kurtosis	2.133688911
Mean	6504.748214
Median Absolute Deviation (MAD)	1652.35
Skewness	1.684436524
Sum	364265.9
Variance	82596505.8
Monotocity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
25705.6	1	1.8%
31322.8	1	1.8%
827.5	1	1.8%
320.1	1	1.8%
15080.8	1	1.8%
1939.7	1	1.8%
278.9	1	1.8%
199.3	1	1.8%
185.2	1	1.8%
98.2	1	1.8%
Other values (46)	46	82.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
98.2	1	1.8%
107.7	1	1.8%
131.6	1	1.8%
150.9	1	1.8%
169.2	1	1.8%

Value	Count	Frequency (%)
36532.3	1	1.8%
31322.8	1	1.8%
28246.4	1	1.8%
25705.6	1	1.8%
23414.4	1	1.8%

Average rate per passenger km in paise
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	55
Distinct (%)	98.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	11.65125
Minimum	1.48
Maximum	31.5
Zeros	0
Zeros (%)	0.0%
Memory size	576.0 B

Quantile statistics

Minimum	1.48
5-th percentile	1.8175
Q1	2.565
median	7.355
Q3	22.3675
95-th percentile	26.475
Maximum	31.5
Range	30.02
Interquartile range (IQR)	19.8025

Descriptive statistics

Standard deviation	9.851850599
Coefficient of variation (CV)	0.8455616864
Kurtosis	-1.393659295
Mean	11.65125
Median Absolute Deviation (MAD)	5.245
Skewness	0.5208817919
Sum	652.47
Variance	97.05896023
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
24.5	2	3.6%
17.09	1	1.8%
24.3	1	1.8%
1.85	1	1.8%
26.3	1	1.8%
2.5	1	1.8%
10.64	1	1.8%
3.97	1	1.8%
2.13	1	1.8%
7.56	1	1.8%
Other values (45)	45	80.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1.48	1	1.8%
1.71	1	1.8%
1.72	1	1.8%
1.85	1	1.8%
2.01	1	1.8%

Value	Count	Frequency (%)
31.5	1	1.8%
28.5	1	1.8%
27	1	1.8%
26.3	1	1.8%
26.1	1	1.8%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	Train Number	Average passengers per day non peak season	Average Kms Per Day	Yearly Passenger In Million	Passenger Kilometers	Fuel Consumption in Litres	Electricity Consumption in Units	Average Lead Distance	Average Time Delay in Minutes Yearly	Average Lead Time in Mins Yearly	Earnings in Crs	Average rate per passenger km in paise
0	1231	412	872	1284	6551	59966	66517	15.9	68.8	51.8	98.2	1.48
1	1232	499	776	1275	8165	54235	62400	16.4	69.9	48.9	107.7	1.72
2	1233	680	914	1594	11770	65895	77665	17.3	72.1	48.7	131.6	1.71
3	1234	764	928	1692	13268	68617	81885	17.4	73.9	48.4	150.9	1.85
4	1235	808	942	1750	13561	70430	83991	16.8	74.8	48.0	169.2	2.01
5	1236	882	990	1872	14460	74128	88588	16.4	74.9	47.3	185.2	2.09
6	1237	954	1038	1992	15791	77698	93489	16.6	74.8	46.9	199.3	2.13
7	1238	1018	1064	2082	17164	79130	96294	16.9	74.4	46.2	219.2	2.28
8	1239	1081	1111	2192	18469	83676	102145	17.1	75.3	46.6	229.3	2.25
9	1240	1106	1151	2257	19068	88095	107163	17.2	76.5	47.4	252.6	2.36

Last rows

	Train Number	Average passengers per day non peak season	Average Kms Per Day	Yearly Passenger In Million	Passenger Kilometers	Fuel Consumption in Litres	Electricity Consumption in Units	Average Lead Distance	Average Time Delay in Minutes Yearly	Average Lead Time in Mins Yearly	Earnings in Crs	Average rate per passenger km in paise
46	1277	3178	2200	5378	103759	471943	575702	32.7	214.5	107.0	14072.5	24.4
47	1278	3329	2396	5725	106419	509195	615614	32.0	212.6	107.5	15080.8	24.5
48	1279	3514	2705	6219	111897	582867	694764	31.8	215.5	111.7	17176.0	24.7
49	1280	3689	2835	6524	119842	650114	769956	32.5	229.3	118.0	19783.3	25.7
50	1281	3802	3118	6920	124836	713196	838032	32.8	228.7	121.1	21866.5	26.1
51	1282	3876	3370	7246	130917	772548	903465	33.8	229.2	124.7	23414.4	25.9
52	1283	4061	3590	7651	137127	841381	978508	33.8	234.4	127.9	25705.6	26.3
53	1284	4377	3847	8224	144057	902465	1046522	32.9	234.6	127.2	28246.4	27.0
54	1285	4477	3944	8421	145654	952449	1098103	32.5	241.5	130.4	31322.8	28.5
55	1286	4552	3845	8397	168589	990153	1158742	37.0	257.5	138.0	36532.3	31.5

Overview

Variables

Interactions

Correlations

Pearson's r

Spearman's ρ

Kendall's τ

Phik (φk)

Missing values

Sample

First rows

Last rows